Evidence map›Paper›PMID 41642861›Full record

ArticleStatistical methods in medical research2026

Monitoring time to event in registry data using CUSUMs based on relative survival models.

Jimmy Huy Tran, Jan Terje Kvaløy, Hartwig Kørner

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jimmy Huy TranDepartment of Mathematics and Physics, University of Stavanger, Norway.ORCID 0009-0007-1565-8998
Jan Terje KvaløyDepartment of Mathematics and Physics, University of Stavanger, Norway.ORCID 0000-0002-8829-6250
Hartwig KørnerDepartment of Gastrointestinal Surgery, Stavanger University Hospital Stavanger, Norway.ORCID 0000-0003-0498-2331

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An aspect of interest in surveillance of diseases is whether the survival time distribution changes over time. By following data in health registries over time, this can be monitored, either in real time or retrospectively. With relevant risk factors registered, these can be taken into account in the monitoring as well. A challenge in monitoring survival times based on registry data is that the information related to cause of death might either be missing or uncertain. To quantify the burden of disease in such cases, relative survival methods can be used, where the total hazard is modelled as the population hazard plus the excess hazard due to the disease.We propose a cumulative sum (CUSUM) procedure for monitoring for changes in the survival time distribution in cases where the use of excess hazard models is relevant. The CUSUM chart is based on a survival log-likelihood ratio and extends previously suggested methods for monitoring of time to event data to the excess hazard setting. The procedure takes into account changes in the population risk over time, as well as changes in the excess hazard which is explained by observed covariates. Properties, challenges and an application to cancer registry data will be presented.

Indexed as

Models, StatisticalRegistriesHumansLikelihood FunctionsNeoplasmsProportional Hazards ModelsSurvival AnalysisTime Factorscancer registry dataExcess hazardrelative survivalrisk-adjusted monitoringsurvival time monitoring

Identifiers

PMID41642861
PMCPMC13047229

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.